TY - JOUR
T1 - Methods to model and predict the ViewRay treatment deliveries to aid patient scheduling and treatment planning
AU - Liu, Shi
AU - Wu, Yu
AU - Omar Wooten, H.
AU - Green, Olga
AU - Archer, Brent
AU - Li, Harold
AU - Yang, Deshan
N1 - Funding Information:
Research reported in this study was partially supported by the Agency for Healthcare Research and Quality (AHRQ) under award 1R01HS0222888, and partially supported by a research grant from ViewRay Incorporated. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency of Healthcare Research and Quality.
Publisher Copyright:
© Creative Commons Attribution 4.0 International License.
PY - 2016
Y1 - 2016
N2 - A software tool is developed, given a new treatment plan, to predict treatment delivery time for radiation therapy (RT) treatments of patients on ViewRay magnetic resonance image-guided radiation therapy (MR-IGRT) delivery system. This tool is necessary for managing patient treatment scheduling in our clinic. The predicted treatment delivery time and the assessment of plan complexities could also be useful to aid treatment planning. A patient's total treatment delivery time, not including time required for localization, is modeled as the sum of four components: 1) the treatment initialization time; 2) the total beam-on time; 3) the gantry rotation time; and 4) the multileaf collimator (MLC) motion time. Each of the four components is predicted separately. The total beam-on time can be calculated using both the planned beam-on time and the decay-corrected dose rate. To predict the remain-ing components, we retrospectively analyzed the patient treatment delivery record files. The initialization time is demonstrated to be random since it depends on the final gantry angle of the previous treatment. Based on modeling the relationships between the gantry rotation angles and the corresponding rotation time, linear regression is applied to predict the gantry rotation time. The MLC motion time is calculated using the leaves delay modeling method and the leaf motion speed. A quantitative analysis was performed to understand the correlation between the total treatment time and the plan complexity. The proposed algorithm is able to predict the ViewRay treatment delivery time with the average prediction error 0.22 min or 1.82%, and the maximal prediction error 0.89 min or 7.88%. The analysis has shown the correlation between the plan modulation (PM) factor and the total treatment delivery time, as well as the treatment delivery duty cycle. A possibility has been identified to significantly reduce MLC motion time by optimizing the positions of closed MLC pairs. The accuracy of the proposed prediction algorithm is sufficient to support patient treatment appointment scheduling. This developed software tool is currently applied in use on a daily basis in our clinic, and could also be used as an important indicator for treatment plan complexity.
AB - A software tool is developed, given a new treatment plan, to predict treatment delivery time for radiation therapy (RT) treatments of patients on ViewRay magnetic resonance image-guided radiation therapy (MR-IGRT) delivery system. This tool is necessary for managing patient treatment scheduling in our clinic. The predicted treatment delivery time and the assessment of plan complexities could also be useful to aid treatment planning. A patient's total treatment delivery time, not including time required for localization, is modeled as the sum of four components: 1) the treatment initialization time; 2) the total beam-on time; 3) the gantry rotation time; and 4) the multileaf collimator (MLC) motion time. Each of the four components is predicted separately. The total beam-on time can be calculated using both the planned beam-on time and the decay-corrected dose rate. To predict the remain-ing components, we retrospectively analyzed the patient treatment delivery record files. The initialization time is demonstrated to be random since it depends on the final gantry angle of the previous treatment. Based on modeling the relationships between the gantry rotation angles and the corresponding rotation time, linear regression is applied to predict the gantry rotation time. The MLC motion time is calculated using the leaves delay modeling method and the leaf motion speed. A quantitative analysis was performed to understand the correlation between the total treatment time and the plan complexity. The proposed algorithm is able to predict the ViewRay treatment delivery time with the average prediction error 0.22 min or 1.82%, and the maximal prediction error 0.89 min or 7.88%. The analysis has shown the correlation between the plan modulation (PM) factor and the total treatment delivery time, as well as the treatment delivery duty cycle. A possibility has been identified to significantly reduce MLC motion time by optimizing the positions of closed MLC pairs. The accuracy of the proposed prediction algorithm is sufficient to support patient treatment appointment scheduling. This developed software tool is currently applied in use on a daily basis in our clinic, and could also be used as an important indicator for treatment plan complexity.
KW - MR-IGRT
KW - Plan complexity
KW - Statistical analysis
KW - Treatment delivery time
KW - ViewRay
UR - http://www.scopus.com/inward/record.url?scp=84962640013&partnerID=8YFLogxK
U2 - 10.1120/jacmp.v17i2.5907
DO - 10.1120/jacmp.v17i2.5907
M3 - Article
C2 - 27074472
AN - SCOPUS:84962640013
SN - 1526-9914
VL - 17
SP - 50
EP - 62
JO - Journal of Applied Clinical Medical Physics
JF - Journal of Applied Clinical Medical Physics
IS - 2
ER -